code: def dnn_columns(include_label_column=False, n_classes=2):
      """Return feature_columns for DNN model."""
      language = tf.feature_column.embedding_column(
          tf.feature_column.categorical_column_with_vocabulary_list(
              key="language", vocabulary_list=("english", "chinese")
          ),
          dimension=1,
      )
      age = tf.feature_column.numeric_column(
          key="age", default_value=0.0
      )
      features = [age, language]
      if include_label_column:
          label = tf.feature_column.numeric_column(
              key="label", default_value=0.0
          )
          if n_classes > 2:
              label = (
                  tf.feature_column.categorical_column_with_identity(
                      key="label", num_buckets=n_classes
                  )
              )
          features.append(label)
      return features
  
file_path: model-analysis/tensorflow_model_analysis/eval_saved_model/example_trainers/util.py
imports: import tensorflow
  from tensorflow_model_analysis.eval_saved_model import export
  from tensorflow_model_analysis.eval_saved_model import util
label_desc: Use image node_id 5 for calling a global function with example usage: dnn_columns(include_label_column, n_classes) and returns: features